Upsampling Lines. To capture holistic structures for higher resolution images, we should upsample the generated priors \(\mathbf {\tilde{P}}\)  to arbitrary scales without obvious degeneration.
blackParticularly, upsampled structures can achieve superior performance as empirically analyzed in Sec. REF .

However, vanilla interpolation-based resizing causes the aliasing effect to lines as well as canny edges as in Fig. REF (f)–(i). Such artifacts are more serious for large image sizes, and deteriorate the inpainted results. To address this issue,
we train a CNN-based SSU to upsample lines to a doubled size. Since lines obtained from a wireframe parser have good discrete representations [1]}, [2]}, i.e., a line can be indicated as positions of two endpoints and their relation, we can draw line maps in various resolutions without any ambiguities as in Fig. REF (d) and Fig. REF (e).
Through the iterative calling, we can get high-quality lines at higher resolutions.
Remarkably, despite lines can be upsampled successfully, canny edges fail to get correct results with the same training strategy as shown in Fig. REF (j).
Because there are ambiguities in the canny edge from different image sizes as in Fig. REF (b) and Fig. REF (c). Interestingly, we find that if the upsampling module is trained in lines, it can also produce smoothed canny edge maps at higher resolution, which thanks to the generalization of the network as in Fig. REF (k).
blackSuch a good property can also be generalized for upsampling L-Edges.
